

from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      []
						for part_id in partition_order:
												snake_case_  =      df.where(F'''SPARK_PARTITION_ID() = {part_id}'''			).collect()
												for row_idx, row in enumerate(SCREAMING_SNAKE_CASE__			):
																		expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict())			)
						return expected_row_ids_and_row_dicts


@require_not_windows
@require_dill_gt_0_3_2
def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      pyspark.sql.SparkSession.builder.master('''local[*]'''			).appName('''pyspark'''			).getOrCreate()
						snake_case_  =      spark.range(100			).repartition(1			)
						snake_case_  =      Spark(SCREAMING_SNAKE_CASE__			)
						# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
						# that each partition can hold 2 rows.
						spark_builder._repartition_df_if_needed(max_shard_size=16			)
						# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
						assert spark_builder.df.rdd.getNumPartitions() == 50


@require_not_windows
@require_dill_gt_0_3_2
def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      pyspark.sql.SparkSession.builder.master('''local[*]'''			).appName('''pyspark'''			).getOrCreate()
						snake_case_  =      spark.range(10			).repartition(2			)
						snake_case_  =      [1, 0]
						snake_case_  =      _generate_iterable_examples(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)  # Reverse the partitions.
						snake_case_  =      _get_expected_row_ids_and_row_dicts_for_partition_order(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)

						for i, (row_id, row_dict) in enumerate(generate_fn()			):
												snake_case_,       snake_case_  =      expected_row_ids_and_row_dicts[i]
												assert row_id == expected_row_id
												assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      pyspark.sql.SparkSession.builder.master('''local[*]'''			).appName('''pyspark'''			).getOrCreate()
						snake_case_  =      spark.range(10			).repartition(1			)
						snake_case_  =      SparkExamplesIterable(SCREAMING_SNAKE_CASE__			)
						assert it.n_shards == 1
						for i, (row_id, row_dict) in enumerate(SCREAMING_SNAKE_CASE__			):
												assert row_id == F'''0_{i}'''
												assert row_dict == {"id": i}


@require_not_windows
@require_dill_gt_0_3_2
def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      pyspark.sql.SparkSession.builder.master('''local[*]'''			).appName('''pyspark'''			).getOrCreate()
						snake_case_  =      spark.range(30			).repartition(3			)
						# Mock the generator so that shuffle reverses the partition indices.
						with patch('''numpy.random.Generator'''			) as generator_mock:
												snake_case_  =      lambda SCREAMING_SNAKE_CASE__			: x.reverse()
												snake_case_  =      _get_expected_row_ids_and_row_dicts_for_partition_order(SCREAMING_SNAKE_CASE__    ,						[2, 1, 0]			)

												snake_case_  =      SparkExamplesIterable(SCREAMING_SNAKE_CASE__			).shuffle_data_sources(SCREAMING_SNAKE_CASE__			)
												assert shuffled_it.n_shards == 3
												for i, (row_id, row_dict) in enumerate(SCREAMING_SNAKE_CASE__			):
																		snake_case_,       snake_case_  =      expected_row_ids_and_row_dicts[i]
																		assert row_id == expected_row_id
																		assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      pyspark.sql.SparkSession.builder.master('''local[*]'''			).appName('''pyspark'''			).getOrCreate()
						snake_case_  =      spark.range(20			).repartition(4			)

						# Partitions 0 and 2
						snake_case_  =      SparkExamplesIterable(SCREAMING_SNAKE_CASE__			).shard_data_sources(worker_id=0    ,						num_workers=2			)
						assert shard_it_a.n_shards == 2
						snake_case_  =      _get_expected_row_ids_and_row_dicts_for_partition_order(SCREAMING_SNAKE_CASE__    ,						[0, 2]			)
						for i, (row_id, row_dict) in enumerate(SCREAMING_SNAKE_CASE__			):
												snake_case_,       snake_case_  =      expected_row_ids_and_row_dicts_a[i]
												assert row_id == expected_row_id
												assert row_dict == expected_row_dict

						# Partitions 1 and 3
						snake_case_  =      SparkExamplesIterable(SCREAMING_SNAKE_CASE__			).shard_data_sources(worker_id=1    ,						num_workers=2			)
						assert shard_it_a.n_shards == 2
						snake_case_  =      _get_expected_row_ids_and_row_dicts_for_partition_order(SCREAMING_SNAKE_CASE__    ,						[1, 3]			)
						for i, (row_id, row_dict) in enumerate(SCREAMING_SNAKE_CASE__			):
												snake_case_,       snake_case_  =      expected_row_ids_and_row_dicts_a[i]
												assert row_id == expected_row_id
												assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      pyspark.sql.SparkSession.builder.master('''local[*]'''			).appName('''pyspark'''			).getOrCreate()
						snake_case_  =      spark.range(100			).repartition(1			)
						snake_case_  =      Spark(SCREAMING_SNAKE_CASE__			)
						# Choose a small max_shard_size for maximum partitioning.
						spark_builder._repartition_df_if_needed(max_shard_size=1			)
						# The new number of partitions should not be greater than the number of rows.
						assert spark_builder.df.rdd.getNumPartitions() == 100







"""simple docstring"""





import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


_SCREAMING_SNAKE_CASE   :							int          =		re.compile(R'''\s+''')







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict					) ->							List[str]:
  return {"hash": hashlib.mda(re.sub(_lowerCamelCase						,							''						,							example['content']					).encode('utf-8'					)					).hexdigest()}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple					) ->							int:
  lowerCamelCase_       	=		[len(_lowerCamelCase					) for line in example['content'].splitlines()]
  return {"line_mean": np.mean(_lowerCamelCase					), "line_max": max(_lowerCamelCase					)}







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[str]					) ->							int:
  lowerCamelCase_       	=		np.mean([c.isalnum() for c in example['content']]					)
  return {"alpha_frac": alpha_frac}







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						Optional[int]					) ->							Optional[Any]:
  if example["hash"] in uniques:
    uniques.remove(example['hash']					)
    return True
  else:
    return False







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any						,							_lowerCamelCase			:						Optional[Any]=5					) ->							int:
  lowerCamelCase_       	=		['auto-generated', 'autogenerated', 'automatically generated']
  lowerCamelCase_       	=		example['content'].splitlines()
  for _, line in zip(range(_lowerCamelCase					)						,							_lowerCamelCase					):
    for keyword in keywords:
      if keyword in line.lower():
        return {"autogenerated": True}
  else:
    return {"autogenerated": False}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						Dict=5						,							_lowerCamelCase			:						List[str]=0.05					) ->							Tuple:
  lowerCamelCase_       	=		['unit tests', 'test file', 'configuration file']
  lowerCamelCase_       	=		example['content'].splitlines()
  lowerCamelCase_       	=		0
  lowerCamelCase_       	=		0
  # first test
  for _, line in zip(range(_lowerCamelCase					)						,							_lowerCamelCase					):
    for keyword in keywords:
      if keyword in line.lower():
        return {"config_or_test": True}
    # second test
  lowerCamelCase_       	=		example['content'].count('\n'					)
  lowerCamelCase_       	=		int(coeff * nlines					)
  for line in lines:
    count_config += line.lower().count('config'					)
    count_test += line.lower().count('test'					)
    if count_config > threshold or count_test > threshold:
      return {"config_or_test": True}
  return {"config_or_test": False}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any					) ->							List[str]:
  lowerCamelCase_       	=		['def ', 'class ', 'for ', 'while ']
  lowerCamelCase_       	=		example['content'].splitlines()
  for line in lines:
    for keyword in keywords:
      if keyword in line.lower():
        return {"has_no_keywords": False}
  return {"has_no_keywords": True}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict						,							_lowerCamelCase			:						Dict=4					) ->							Optional[Any]:
  lowerCamelCase_       	=		example['content'].splitlines()
  lowerCamelCase_       	=		0
  for line in lines:
    counter += line.lower().count('='					)
    if counter > minimum:
      return {"has_few_assignments": False}
  return {"has_few_assignments": True}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict					) ->							List[str]:
  lowerCamelCase_       	=		tokenizer(example['content']						,							truncation=_lowerCamelCase					)['input_ids']
  lowerCamelCase_       	=		len(example['content']					) / len(_lowerCamelCase					)
  return {"ratio": ratio}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[Any]					) ->							List[Any]:
  lowerCamelCase_       	=		{}
  results.update(get_hash(_lowerCamelCase					)					)
  results.update(line_stats(_lowerCamelCase					)					)
  results.update(alpha_stats(_lowerCamelCase					)					)
  results.update(char_token_ratio(_lowerCamelCase					)					)
  results.update(is_autogenerated(_lowerCamelCase					)					)
  results.update(is_config_or_test(_lowerCamelCase					)					)
  results.update(has_no_keywords(_lowerCamelCase					)					)
  results.update(has_few_assignments(_lowerCamelCase					)					)
  return results







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any						,							_lowerCamelCase			:						Dict						,							_lowerCamelCase			:						Optional[Any]					) ->							Any:
  if not check_uniques(_lowerCamelCase						,							_lowerCamelCase					):
    return False
  elif example["autogenerated"]:
    return False
  elif example["line_max"] > args.line_max:
    return False
  elif example["line_mean"] > args.line_mean:
    return False
  elif example["alpha_frac"] < args.alpha_frac:
    return False
  elif example["ratio"] < args.min_token_ratio:
    return False
  elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_few_assignments"]:
    return False
  else:
    return True







def 	lowerCamelCase__			(				_lowerCamelCase			:						str					) ->							int:
  with open(_lowerCamelCase						,							'rb'					) as f_in:
    with gzip.open(str(_lowerCamelCase					) + '.gz'						,							'wb'						,							compresslevel=6					) as f_out:
      shutil.copyfileobj(_lowerCamelCase						,							_lowerCamelCase					)
  os.unlink(_lowerCamelCase					)


# Settings
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		HfArgumentParser(PreprocessingArguments)
_SCREAMING_SNAKE_CASE   :							Any          =		parser.parse_args()
if args.num_workers is None:
  _SCREAMING_SNAKE_CASE   :							List[str]          =		multiprocessing.cpu_count()
_SCREAMING_SNAKE_CASE   :							str          =		AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		time.time()
_SCREAMING_SNAKE_CASE   :							int          =		load_dataset(args.dataset_name, split='''train''')
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		time.time()
_SCREAMING_SNAKE_CASE   :							Tuple          =		ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
_SCREAMING_SNAKE_CASE   :							List[Any]          =		set(ds.unique('''hash'''))
_SCREAMING_SNAKE_CASE   :							Optional[int]          =		len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
_SCREAMING_SNAKE_CASE   :							Dict          =		time.time()
_SCREAMING_SNAKE_CASE   :							Tuple          =		ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(F'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		time.time()
  _SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		deduplicate_dataset(ds_filter, args.jaccard_threshold)
  print(F'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
  print(F'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
_SCREAMING_SNAKE_CASE   :							Dict          =		Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
  with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
    json.dump(duplicate_clusters, f)

_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
  _SCREAMING_SNAKE_CASE   :							Dict          =		str(data_dir / F'''file-{file_number+1:012}.json''')
  _SCREAMING_SNAKE_CASE   :							Any          =		min(len(ds_filter), index + args.samples_per_file)
  ds_filter.select(list(range(index, end_index))).to_json(file_path)
  compress_file(file_path)






print(F'''Time to save dataset: {time.time()-t_start:.2f}''')




